Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations5245
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory368.8 KiB
Average record size in memory72.0 B

Variable types

Numeric8

Alerts

Humidity is highly overall correlated with Soil_Temperature and 1 other fieldsHigh correlation
Soil_Temperature is highly overall correlated with Humidity and 1 other fieldsHigh correlation
Temperature is highly overall correlated with Humidity and 1 other fieldsHigh correlation
Wind_Speed_kmh has 191 (3.6%) zeros Zeros
Wind_Dir_Sin has 187 (3.6%) zeros Zeros
Precipitation_log has 4985 (95.0%) zeros Zeros

Reproduction

Analysis started2025-05-04 14:39:06.061200
Analysis finished2025-05-04 14:39:09.953275
Duration3.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

High correlation 

Distinct4727
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.757754
Minimum0.395
Maximum40.659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-05-04T11:39:09.995073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.395
5-th percentile6.6722
Q111.682
median16.005
Q321.044
95-th percentile30.039
Maximum40.659
Range40.264
Interquartile range (IQR)9.362

Descriptive statistics

Standard deviation6.9715645
Coefficient of variation (CV)0.41602023
Kurtosis-0.063909847
Mean16.757754
Median Absolute Deviation (MAD)4.644
Skewness0.50128346
Sum87894.418
Variance48.602712
MonotonicityNot monotonic
2025-05-04T11:39:10.067403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.328 4
 
0.1%
13.234 3
 
0.1%
14.449 3
 
0.1%
11.745 3
 
0.1%
12.574 3
 
0.1%
21.236 3
 
0.1%
13.855 3
 
0.1%
16.522 3
 
0.1%
16.373 3
 
0.1%
22.192 3
 
0.1%
Other values (4717) 5214
99.4%
ValueCountFrequency (%)
0.395 1
< 0.1%
0.434 1
< 0.1%
0.458 1
< 0.1%
0.481 1
< 0.1%
0.576 1
< 0.1%
0.707 1
< 0.1%
0.794 1
< 0.1%
0.825 1
< 0.1%
1.052 1
< 0.1%
1.139 1
< 0.1%
ValueCountFrequency (%)
40.659 1
< 0.1%
39.124 1
< 0.1%
38.948 1
< 0.1%
38.651 1
< 0.1%
38.441 1
< 0.1%
38.412 1
< 0.1%
38.092 1
< 0.1%
38.009 1
< 0.1%
37.783 1
< 0.1%
37.711 1
< 0.1%

Humidity
Real number (ℝ)

High correlation 

Distinct3255
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.530276
Minimum14.5
Maximum99.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-05-04T11:39:10.138239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile28.4534
Q146.117
median61.933
Q376.65
95-th percentile94.467
Maximum99.95
Range85.45
Interquartile range (IQR)30.533

Descriptive statistics

Standard deviation19.931637
Coefficient of variation (CV)0.3239322
Kurtosis-0.85861239
Mean61.530276
Median Absolute Deviation (MAD)15.2
Skewness-0.044990882
Sum322726.29
Variance397.27017
MonotonicityNot monotonic
2025-05-04T11:39:10.210858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.417 6
 
0.1%
63.667 6
 
0.1%
62 6
 
0.1%
65.05 6
 
0.1%
97.3 6
 
0.1%
60.8 6
 
0.1%
70.967 6
 
0.1%
56.417 6
 
0.1%
45.117 5
 
0.1%
79.45 5
 
0.1%
Other values (3245) 5187
98.9%
ValueCountFrequency (%)
14.5 1
< 0.1%
15.95 1
< 0.1%
16.183 1
< 0.1%
16.417 1
< 0.1%
17.133 1
< 0.1%
17.183 1
< 0.1%
17.25 1
< 0.1%
17.317 1
< 0.1%
17.333 1
< 0.1%
17.483 1
< 0.1%
ValueCountFrequency (%)
99.95 2
< 0.1%
99.917 1
< 0.1%
99.9 1
< 0.1%
99.783 1
< 0.1%
99.667 1
< 0.1%
99.633 2
< 0.1%
99.583 1
< 0.1%
99.5 1
< 0.1%
99.367 1
< 0.1%
99.233 1
< 0.1%

Wind_Speed_kmh
Real number (ℝ)

Zeros 

Distinct1927
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5337643
Minimum-0.171
Maximum5.39
Zeros191
Zeros (%)3.6%
Negative1
Negative (%)< 0.1%
Memory size82.0 KiB
2025-05-04T11:39:10.279521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.171
5-th percentile0.037
Q10.783
median1.605
Q32.215
95-th percentile2.942
Maximum5.39
Range5.561
Interquartile range (IQR)1.432

Descriptive statistics

Standard deviation0.91697903
Coefficient of variation (CV)0.59786175
Kurtosis-0.55325329
Mean1.5337643
Median Absolute Deviation (MAD)0.693
Skewness0.08093105
Sum8044.594
Variance0.84085055
MonotonicityNot monotonic
2025-05-04T11:39:10.348860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 191
 
3.6%
0.047 10
 
0.2%
2.262 9
 
0.2%
2.13 9
 
0.2%
1.873 9
 
0.2%
1.877 9
 
0.2%
2.113 9
 
0.2%
1.233 9
 
0.2%
1.693 9
 
0.2%
2.095 8
 
0.2%
Other values (1917) 4973
94.8%
ValueCountFrequency (%)
-0.171 1
 
< 0.1%
0 191
3.6%
0.002 3
 
0.1%
0.003 1
 
< 0.1%
0.005 3
 
0.1%
0.007 4
 
0.1%
0.008 4
 
0.1%
0.01 5
 
0.1%
0.012 3
 
0.1%
0.013 5
 
0.1%
ValueCountFrequency (%)
5.39 1
< 0.1%
5.093 1
< 0.1%
5.017 1
< 0.1%
4.892 1
< 0.1%
4.887 1
< 0.1%
4.795 1
< 0.1%
4.553 1
< 0.1%
4.54 1
< 0.1%
4.465 1
< 0.1%
4.438 1
< 0.1%

Soil_Moisture
Real number (ℝ)

Distinct1120
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7857.2744
Minimum6546.667
Maximum8946.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-05-04T11:39:10.419219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6546.667
5-th percentile6722.667
Q17258.667
median7920
Q38469.333
95-th percentile8765.333
Maximum8946.667
Range2400
Interquartile range (IQR)1210.666

Descriptive statistics

Standard deviation697.80649
Coefficient of variation (CV)0.088810248
Kurtosis-1.3162553
Mean7857.2744
Median Absolute Deviation (MAD)584
Skewness-0.30215178
Sum41211404
Variance486933.9
MonotonicityNot monotonic
2025-05-04T11:39:10.491692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8565.333 26
 
0.5%
6736 26
 
0.5%
8562.667 25
 
0.5%
6728 22
 
0.4%
8466.667 21
 
0.4%
8560 19
 
0.4%
8621.333 19
 
0.4%
8317.333 18
 
0.3%
8448 18
 
0.3%
8450.667 18
 
0.3%
Other values (1110) 5033
96.0%
ValueCountFrequency (%)
6546.667 1
< 0.1%
6557.333 1
< 0.1%
6562.667 2
< 0.1%
6564.171 1
< 0.1%
6565.333 2
< 0.1%
6568 1
< 0.1%
6569.022 1
< 0.1%
6573.333 2
< 0.1%
6575.828 1
< 0.1%
6576 1
< 0.1%
ValueCountFrequency (%)
8946.667 1
 
< 0.1%
8941.333 3
0.1%
8936 1
 
< 0.1%
8933.333 1
 
< 0.1%
8930.667 1
 
< 0.1%
8928 1
 
< 0.1%
8925.333 2
< 0.1%
8922.667 1
 
< 0.1%
8920 3
0.1%
8914.667 1
 
< 0.1%

Soil_Temperature
Real number (ℝ)

High correlation 

Distinct3816
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.977685
Minimum1.33
Maximum51.047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2025-05-04T11:39:10.561840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile7.1104
Q111.757
median15.64
Q320.413
95-th percentile31.717
Maximum51.047
Range49.717
Interquartile range (IQR)8.656

Descriptive statistics

Standard deviation7.6944529
Coefficient of variation (CV)0.45320979
Kurtosis2.3491688
Mean16.977685
Median Absolute Deviation (MAD)4.292
Skewness1.2560893
Sum89047.957
Variance59.204606
MonotonicityNot monotonic
2025-05-04T11:39:10.636378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.965 7
 
0.1%
15.058 7
 
0.1%
13.08 6
 
0.1%
15.747 6
 
0.1%
11.34 6
 
0.1%
12.215 6
 
0.1%
16.358 6
 
0.1%
14.507 5
 
0.1%
13.257 5
 
0.1%
13.372 5
 
0.1%
Other values (3806) 5186
98.9%
ValueCountFrequency (%)
1.33 1
< 0.1%
1.34 1
< 0.1%
1.402 1
< 0.1%
1.422 1
< 0.1%
1.62 1
< 0.1%
1.622 1
< 0.1%
1.715 1
< 0.1%
1.84 1
< 0.1%
1.955 1
< 0.1%
2.048 1
< 0.1%
ValueCountFrequency (%)
51.047 1
< 0.1%
51.018 1
< 0.1%
50.507 1
< 0.1%
50.422 1
< 0.1%
50.048 1
< 0.1%
50.003 1
< 0.1%
49.672 1
< 0.1%
49.652 1
< 0.1%
49.33 1
< 0.1%
49.288 1
< 0.1%

Wind_Dir_Sin
Real number (ℝ)

Zeros 

Distinct1572
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.12912267
Minimum-1
Maximum1
Zeros187
Zeros (%)3.6%
Negative2806
Negative (%)53.5%
Memory size82.0 KiB
2025-05-04T11:39:10.707757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.97300831
Q1-0.68412714
median-0.19936793
Q30.44358746
95-th percentile0.84495393
Maximum1
Range2
Interquartile range (IQR)1.1277146

Descriptive statistics

Standard deviation0.59767488
Coefficient of variation (CV)-4.628737
Kurtosis-1.3831648
Mean-0.12912267
Median Absolute Deviation (MAD)0.50773885
Skewness0.19201694
Sum-677.24841
Variance0.35721527
MonotonicityNot monotonic
2025-05-04T11:39:10.782456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 187
 
3.6%
-0.7071067812 157
 
3.0%
0.7071067812 47
 
0.9%
-0.7029851951 45
 
0.9%
-0.7050427765 41
 
0.8%
-0.6904658973 33
 
0.6%
-0.6967520764 33
 
0.6%
-0.7009092643 29
 
0.6%
-0.6946583705 29
 
0.6%
-0.6925587631 29
 
0.6%
Other values (1562) 4615
88.0%
ValueCountFrequency (%)
-1 5
0.1%
-0.9999957523 2
 
< 0.1%
-0.9999906324 1
 
< 0.1%
-0.9999864744 1
 
< 0.1%
-0.9999831107 1
 
< 0.1%
-0.9999619231 3
0.1%
-0.9999322403 4
0.1%
-0.9999312206 1
 
< 0.1%
-0.9999179439 1
 
< 0.1%
-0.9998943165 2
 
< 0.1%
ValueCountFrequency (%)
1 6
0.1%
0.9999957523 1
 
< 0.1%
0.9999831107 1
 
< 0.1%
0.9999619231 2
 
< 0.1%
0.9998943165 1
 
< 0.1%
0.9997293765 1
 
< 0.1%
0.9995767809 2
 
< 0.1%
0.999390827 1
 
< 0.1%
0.9992848605 1
 
< 0.1%
0.9991711151 3
0.1%

Wind_Dir_Cos
Real number (ℝ)

Distinct1564
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.20009003
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative3145
Negative (%)60.0%
Memory size82.0 KiB
2025-05-04T11:39:10.854942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.99389759
Q1-0.89363003
median-0.63158286
Q30.71120448
95-th percentile0.94906031
Maximum1
Range2
Interquartile range (IQR)1.6048345

Descriptive statistics

Standard deviation0.76567351
Coefficient of variation (CV)-3.826645
Kurtosis-1.6262902
Mean-0.20009003
Median Absolute Deviation (MAD)0.35610548
Skewness0.39280765
Sum-1049.4722
Variance0.58625593
MonotonicityNot monotonic
2025-05-04T11:39:10.928674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 187
 
3.6%
0.7071067812 157
 
3.0%
-0.7071067812 47
 
0.9%
0.7112044822 45
 
0.9%
0.7091647787 41
 
0.8%
0.7233649457 33
 
0.6%
0.717312027 33
 
0.6%
0.7193398003 29
 
0.6%
-0.9205048535 29
 
0.6%
0.7213614626 29
 
0.6%
Other values (1554) 4615
88.0%
ValueCountFrequency (%)
-1 29
0.6%
-0.9999999976 1
 
< 0.1%
-0.9999993761 1
 
< 0.1%
-0.9999988472 1
 
< 0.1%
-0.9999957523 4
 
0.1%
-0.9999831107 4
 
0.1%
-0.999973895 1
 
< 0.1%
-0.9999702445 1
 
< 0.1%
-0.9999619231 5
 
0.1%
-0.9999617706 1
 
< 0.1%
ValueCountFrequency (%)
1 187
3.6%
0.9999957523 2
 
< 0.1%
0.9998476952 1
 
< 0.1%
0.9990482216 2
 
< 0.1%
0.9989168406 1
 
< 0.1%
0.9986295348 1
 
< 0.1%
0.9984727495 1
 
< 0.1%
0.9981347984 1
 
< 0.1%
0.996444499 1
 
< 0.1%
0.995112609 1
 
< 0.1%

Precipitation_log
Real number (ℝ)

Zeros 

Distinct63
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00392828
Minimum-0.046043939
Maximum0.60867802
Zeros4985
Zeros (%)95.0%
Negative53
Negative (%)1.0%
Memory size82.0 KiB
2025-05-04T11:39:11.000901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.046043939
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.60867802
Range0.65472196
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.029773786
Coefficient of variation (CV)7.5793441
Kurtosis150.63753
Mean0.00392828
Median Absolute Deviation (MAD)0
Skewness11.104134
Sum20.603829
Variance0.00088647834
MonotonicityNot monotonic
2025-05-04T11:39:11.071639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4985
95.0%
0.04592893189 78
 
1.5%
0.08892620919 24
 
0.5%
-0.002002002671 19
 
0.4%
0.1310282624 18
 
0.3%
0.1705863006 9
 
0.2%
0.2460785226 8
 
0.2%
-0.00300450902 8
 
0.2%
0.001998002663 5
 
0.1%
-0.001000500334 5
 
0.1%
Other values (53) 86
 
1.6%
ValueCountFrequency (%)
-0.0460439385 1
< 0.1%
-0.03978087001 1
< 0.1%
-0.03666398437 1
< 0.1%
-0.03562717764 1
< 0.1%
-0.03252319171 1
< 0.1%
-0.02634397534 1
< 0.1%
-0.02531780798 1
< 0.1%
-0.02429269257 2
< 0.1%
-0.02224560895 1
< 0.1%
-0.01918281942 1
< 0.1%
ValueCountFrequency (%)
0.6086780239 1
< 0.1%
0.5294510879 1
< 0.1%
0.5122246447 1
< 0.1%
0.5019866751 1
< 0.1%
0.4731237566 1
< 0.1%
0.4440445901 1
< 0.1%
0.4134332778 1
< 0.1%
0.3825376035 2
< 0.1%
0.3499523982 1
< 0.1%
0.3293037471 1
< 0.1%

Interactions

2025-05-04T11:39:09.240161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.147145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.610276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.024144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.446228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.877316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.309698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.776231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.298367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.206265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.663621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.078263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.503104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.931749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.371235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.837734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.349303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.260535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.712137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.127134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.553059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.982929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.425727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.891796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.402880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.316177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.761119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.177642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.605904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.033991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.482850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.949008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.456264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.373960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.812566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.229322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.656479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.087147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.540945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.005512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.509902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.429029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.861781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.281194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.708158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.137126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.598333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.062708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.567798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.490928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.917426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.336731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.766029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.199235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.657133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.122171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.626930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.551425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:06.971891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.393642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:07.822607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.256949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:08.718285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-04T11:39:09.182576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-04T11:39:11.124847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
HumidityPrecipitation_logSoil_MoistureSoil_TemperatureTemperatureWind_Dir_CosWind_Dir_SinWind_Speed_kmh
Humidity1.0000.2160.305-0.801-0.8400.304-0.193-0.274
Precipitation_log0.2161.0000.069-0.128-0.147-0.025-0.019-0.100
Soil_Moisture0.3050.0691.000-0.449-0.424-0.0200.007-0.048
Soil_Temperature-0.801-0.128-0.4491.0000.978-0.2860.2520.152
Temperature-0.840-0.147-0.4240.9781.000-0.3070.2570.156
Wind_Dir_Cos0.304-0.025-0.020-0.286-0.3071.000-0.3900.280
Wind_Dir_Sin-0.193-0.0190.0070.2520.257-0.3901.000-0.340
Wind_Speed_kmh-0.274-0.100-0.0480.1520.1560.280-0.3401.000

Missing values

2025-05-04T11:39:09.873213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-04T11:39:09.923390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureHumidityWind_Speed_kmhSoil_MoistureSoil_TemperatureWind_Dir_SinWind_Dir_CosPrecipitation_log
Timestamp
2024-05-30 00:00:009.54267.4830.7558312.0009.4300.216440-0.9762960.0
2024-05-30 00:30:007.38174.9830.7088290.6676.4650.846500-0.5323890.0
2024-05-30 01:00:006.22479.4500.7658301.3336.2370.482258-0.8760290.0
2024-05-30 01:30:005.30885.1670.4988304.0006.0170.681998-0.7313540.0
2024-05-30 02:00:005.21886.5170.1258298.6676.3530.673438-0.7392430.0
2024-05-30 02:30:005.52787.0170.0008290.6676.4450.559193-0.8290380.0
2024-05-30 03:00:004.95787.8670.5938298.6675.4230.6626200.7489560.0
2024-05-30 03:30:003.77789.2001.1778290.6674.4830.084252-0.9964440.0
2024-05-30 04:00:003.05590.7331.1578304.0003.9130.130526-0.9914450.0
2024-05-30 04:30:002.61291.4501.0578282.6673.6630.8571670.5150380.0
TemperatureHumidityWind_Speed_kmhSoil_MoistureSoil_TemperatureWind_Dir_SinWind_Dir_CosPrecipitation_log
Timestamp
2024-09-16 01:30:0011.73376.2501.0487824.00011.757-0.866025-0.5000000.0
2024-09-16 02:00:0010.84578.5172.0187848.00011.070-0.8338860.5519370.0
2024-09-16 02:30:0010.16580.1172.3487874.66710.445-0.5994850.8003860.0
2024-09-16 03:00:009.49481.4001.7257877.3339.930-0.6494480.7604060.0
2024-09-16 03:30:008.85981.8332.1087904.0009.392-0.3474760.9376890.0
2024-09-16 04:00:008.33382.5502.1807901.3338.902-0.5664060.8241260.0
2024-09-16 04:30:007.92281.5002.4037914.6678.470-0.5519370.8338860.0
2024-09-16 05:00:007.60381.7502.2757922.6678.222-0.5783280.8158050.0
2024-09-16 05:30:007.33380.9332.3557917.3337.902-0.4435870.8962310.0
2024-09-16 06:00:007.11680.7332.5907922.6677.683-0.5299190.8480480.0